The ability of analyzing skin lesions in order to differentiate malignant lesions (cancer or pre-cancer condition) over suspect nevus or benign ones may play a significant role in healthcare-based applications.
Currently, physicians such as oncologists and/or dermatologists may provide a subjective analysis of a nevus with heuristic rules such as, e.g., ABCDE (Asymmetry, Borders, Colors, Diameter, Evolving) to describe a nevus as benign or suspect or malignant. Also, physicians may perform a careful examination of, e.g., nevus by means of dermoscopy, with the personal experience of the physician playing a key role in the evaluation.
Clinical diagnosis of melanoma is thus regarded as generally difficult see, e.g., AIOM Linee Guida (Guidelines) “Melanoma” Ed. 2015, p. 9 (available at www.aiom.it) or C. G. Duff, et al.: “A 6 year prospective analysis of the diagnosis of malignant melanoma in pigmented-lesion clinic: even the experts miss malignant melanoma, but not often.” Br J Plas Surg 2001; 54:317-321, with diagnosis directly influenced by the practitioner's experience with a sensitivity between 50 and 85%.
The possibility of reducing the impact of the “human factor”, e.g., the heuristic experience of the physician by providing some sort of technical assistance to the medical diagnosis has been investigated to some extent.
For instance, using image features to differentiate malignant skin lesion from atypical/suspect ones has been proposed.
While somehow related to the ABCDE rule, certain techniques may use image features to perform analysis in manner which basically differs from the ABCDE rule. Also, certain proposed procedures perform semi-automatic classification of nevus based on joined clinical/algorithm results and/or propose a nevus classification based on texture analysis of nevus dermoscopy. Certain procedures use a multi-classifier approach based on shape geometry of the nevus as well as a subset of image features (physiological and dermatological.
The related techniques may require fairly extensive hardware (fiberscope, photo-acoustic hardware, various devices for image analysis in the frequency domain, etc. . . . ) and may use statistical recursive CPU-consuming algorithms based on clustering (K-means) or learning (SOM, Artificial Neural Networks—ANN's).
A general survey of recent developments in that area is provided, e.g., in S. Binu Sathiya, et al.: “A survey on recent computer-aided diagnosis of Melanoma”—2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), Kumaracoil, Thuckalay Tamilnadu Kanyakumari District, India, pp. 1387-1392.
Specifically, various solutions proposed in the literature may be exposed to one or more of the following drawbacks:                a training-set or CPU intensive/consuming ANN learning may be required;        possible “over-fitting”, training set configuration, centroids of clustering and/or segmentation due to hair in the nevus dermoscopy may represent issues of concern;        the results may exhibit low sensitivity and accuracy in, e.g., nevus classification, especially for suspected skin lesions which are neither benign nor melanoma;        in most instances, the outcome may be a “binary” result: melanoma or NOT melanoma, which may be unsatisfactory, e.g., for nevus which, while not benign may require monitoring as it cannot be classified as “melanoma” skin lesion yet;        information about nevus does not include information about future evolution, e.g., a score of possible evolution of analyzed nevus into malignant skin lesion such as melanoma;        follow-up of the analyzed skin lesion is not provided or, if provided, is affected by issues of image registration, that is synchronizing two sets of diagnostic images taken at distinct times.        
For instance, comparing in an accurate manner, e.g., CT scan images or MRI images may require “putting in register” (that is aligning) the images by making it possible to superpose the various slices in order to permit, e.g., an automated algorithm-based comparison.
Similarly, conventional methods of analyzing dermoscopic images may require that, e.g., nevus images taken at different times should be adapted to be superposed or have registration point making it possible to superpose at least the ROI (Region Of Interest) of the images so that results obtained by an analysis algorithm may be compared.